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    pdftitle={DATA STREAM MINING},    % title
    pdfauthor={Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer},     % author
    pdfsubject={},   % subject of the document
    pdfcreator={Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer},   % creator of the document
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\long\def\BEGINOMIT#1\ENDOMIT{\relax}  % to omit large portions of text
%\newtheorem{definition}{Definition}{}
\title{
%\includegraphics[height=3cm]{LogoMOA.jpg} \\
%\includegraphics[height=4cm]{Waikato.jpg} \\ \vspace{2cm}
%\includegraphics[height=2cm]{LogoMOA.jpg} \\
\textbf{DATA STREAM MINING\\ A Practical Approach} %\\ Manual 
 }
\author{Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer}
\date{May 2011}%{August 2009 }%\\ \vspace{3cm} \includegraphics[height=1cm]{cosi_logo.png} }

\begin{document}
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\lstset{language=Java,basicstyle=\tiny,numbers=left}



\pdfbookmark[0]{Titlepage}{title} 
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\pagenumbering{roman}
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%\ENDOMIT
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\pdfbookmark[0]{Contents}{contents}
\tableofcontents
\cleardoublepage
%\listoffigures
%\listoftables
\pagenumbering{arabic}

%--- Macros -----------------------
\def\adwin{{\tt ADWIN }}
\def\thesis{text }
\def\thesisc{text}
\input{ConMacros}

%--- Chapters -----------------------
%\pagestyle{headings}
%\pagenumbering{arabic}
\chapter*{Introduction}
\pdfbookmark[0]{Introduction}{introduction}
\thispagestyle{empty}

{\bf M}assive {\bf O}nline {\bf A}nalysis (MOA) is a %framework 
software environment for implementing algorithms and running experiments
for online learning from %continuous supplies of examples, such as 
evolving data streams.
%The data stream evaluation framework and all algorithms evaluated in this paper
%were implemented in the Java programming language extending the MOA software. %framework.

\begin{center}
\includegraphics[height=2cm]{figures/LogoMOA.jpg} \end{center}

MOA includes a collection of offline and online methods as well as tools for evaluation. 
In particular, it implements boosting, bagging, and Hoeffding Trees, all %both 
with and without Na{\"\i}ve Bayes classifiers at the leaves. 
%MOA  graphical user interface is shown in Figure~\ref{fig:moagui}.
%However, a command line interface is also available.

%\begin{figure}[t]
%\begin{center} 
%\epsfig{file=MOATL, scale=0.3}
%\end{center} 
%\caption{MOA Graphical User Interface}
%\label{fig:moagui}
%\end{figure} 

MOA is related to WEKA, the Waikato
Environment for Knowledge Analysis, which is an award-winning open-source 
workbench containing implementations of a wide range of batch machine 
learning methods. WEKA is also written in Java. The main benefits
of Java are portability, where applications can be run on any platform with
an appropriate Java virtual machine, and the strong and well-developed support 
libraries. Use of the language is widespread, and features such as the
automatic garbage collection help to reduce programmer burden and error.

This text explains the theoretical and practical foundations of the methods and streams available in MOA.
The moa and the weka are both birds native to New Zealand. The weka is a cheeky bird of similar size to a chicken. The moa was a large ostrich-like bird, an order of magnitude larger than a weka, that was hunted to extinction.

\BEGINOMIT
    One of the key data structures used in MOA is the description of an example
from a data stream. This structure borrows from WEKA, where an example is
represented by an array of double precision floating point values. This provides
freedom to store all necessary types of value \--- numeric attribute values can be
stored directly, and discrete attribute values and class labels are represented
by integer index values that are stored as floating point values in the array.
Double precision floating point values require storage space of 64 bits, or 8
bytes. This detail can have implications for memory usage. %utilization.
\ENDOMIT

\part{Introduction and Preliminaries} 
\include{introduction} %\cleardoublepage{}
\input{experimentalsetting} 
\input{ExpSetting}%\cleardoublepage{}
\part{Stationary Data Stream Learning} 
\include{hoeffdingtrees} %\cleardoublepage{}
\include{numerichandling} %\cleardoublepage{}
\include{predstrat} %\cleardoublepage{}
\include{improvebackground} %\cleardoublepage{}
%\include{improvecompare} \cleardoublepage{}
%\include{conclusions} \cleardoublepage{}
\part{Evolving Data Stream Learning} 
\input{MiningDataStreams}
\input{ConceptDrift}

\input{Adwin}
\input{DecisionTrees}
\input{EnsembleMethods}
\input{Stacking}
\input{Leveraging}
\input{TwitterMining}
%--- Bibliography -------------------
%\addcontentsline{toc}{chapter}{\bibname}
\cleardoublepage

%\bibliography{thesis}

%\bibliographystyle{alpha}%plain}
\phantomsection % Ensures that a PDF bookmark is set here
\addcontentsline{toc}{part}{Bibliography}
\bibliographystyle{plain}
\bibliography{StreamMining}

\end{document}